1
|
Andreozzi F, Mancuso E, Rubino M, Salvatori B, Morettini M, Monea G, Göbl C, Mannino GC, Tura A. Glucagon kinetics assessed by mathematical modelling during oral glucose administration in people spanning from normal glucose tolerance to type 2 diabetes. Front Endocrinol (Lausanne) 2024; 15:1376530. [PMID: 38681771 PMCID: PMC11045965 DOI: 10.3389/fendo.2024.1376530] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/25/2024] [Accepted: 03/28/2024] [Indexed: 05/01/2024] Open
Abstract
Background/Objectives Glucagon is important in the maintenance of glucose homeostasis, with also effects on lipids. In this study, we aimed to apply a recently developed model of glucagon kinetics to determine the sensitivity of glucagon variations (especially, glucagon inhibition) to insulin levels ("alpha-cell insulin sensitivity"), during oral glucose administration. Subjects/Methods We studied 50 participants (spanning from normal glucose tolerance to type 2 diabetes) undergoing frequently sampled 5-hr oral glucose tolerance test (OGTT). The alpha-cell insulin sensitivity and the glucagon kinetics were assessed by a mathematical model that we developed previously. Results The alpha-cell insulin sensitivity parameter (named SGLUCA; "GLUCA": "glucagon") was remarkably variable among participants (CV=221%). SGLUCA was found inversely correlated with the mean glycemic values, as well as with 2-hr glycemia of the OGTT. When stratifying participants into two groups (normal glucose tolerance, NGT, N=28, and impaired glucose regulation/type 2 diabetes, IGR_T2D, N=22), we found that SGLUCA was lower in the latter (1.50 ± 0.50·10-2 vs. 0.26 ± 0.14·10-2 ng·L-1 GLUCA/pmol·L-1 INS, in NGT and IGR_T2D, respectively, p=0.009; "INS": "insulin"). Conclusions The alpha-cell insulin sensitivity is highly variable among subjects, and it is different in groups at different glucose tolerance. This may be relevant for defining personalized treatment schemes, in terms of dietary prescriptions but also for treatments with glucagon-related agents.
Collapse
Affiliation(s)
- Francesco Andreozzi
- Department of Medical and Surgical Sciences, Magna Graecia University of Catanzaro, Catanzaro, Italy
| | - Elettra Mancuso
- Department of Medical and Surgical Sciences, Magna Graecia University of Catanzaro, Catanzaro, Italy
| | - Mariangela Rubino
- Department of Medical and Surgical Sciences, Magna Graecia University of Catanzaro, Catanzaro, Italy
| | | | - Micaela Morettini
- Department of Information Engineering, Università Politecnica delle Marche, Ancona, Italy
| | - Giuseppe Monea
- Department of Medical and Surgical Sciences, Magna Graecia University of Catanzaro, Catanzaro, Italy
| | - Christian Göbl
- Department of Obstetrics and Gynaecology, Medical University of Vienna, Vienna, Austria
- Department of Obstetrics and Gynaecology, Medical University of Graz, Graz, Austria
| | - Gaia Chiara Mannino
- Department of Medical and Surgical Sciences, Magna Graecia University of Catanzaro, Catanzaro, Italy
| | - Andrea Tura
- CNR Institute of Neuroscience, Padova, Italy
| |
Collapse
|
2
|
Li S, Dragan I, Tran VDT, Fung CH, Kuznetsov D, Hansen MK, Beulens JWJ, Hart LM‘, Slieker RC, Donnelly LA, Gerl MJ, Klose C, Mehl F, Simons K, Elders PJM, Pearson ER, Rutter GA, Ibberson M. Multi-omics subgroups associated with glycaemic deterioration in type 2 diabetes: an IMI-RHAPSODY Study. Front Endocrinol (Lausanne) 2024; 15:1350796. [PMID: 38510703 PMCID: PMC10951062 DOI: 10.3389/fendo.2024.1350796] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/06/2023] [Accepted: 02/22/2024] [Indexed: 03/22/2024] Open
Abstract
Introduction Type 2 diabetes (T2D) onset, progression and outcomes differ substantially between individuals. Multi-omics analyses may allow a deeper understanding of these differences and ultimately facilitate personalised treatments. Here, in an unsupervised "bottom-up" approach, we attempt to group T2D patients based solely on -omics data generated from plasma. Methods Circulating plasma lipidomic and proteomic data from two independent clinical cohorts, Hoorn Diabetes Care System (DCS) and Genetics of Diabetes Audit and Research in Tayside Scotland (GoDARTS), were analysed using Similarity Network Fusion. The resulting patient network was analysed with Logistic and Cox regression modelling to explore relationships between plasma -omic profiles and clinical characteristics. Results From a total of 1,134 subjects in the two cohorts, levels of 180 circulating plasma lipids and 1195 proteins were used to separate patients into two subgroups. These differed in terms of glycaemic deterioration (Hazard Ratio=0.56;0.73), insulin sensitivity and secretion (C-peptide, p=3.7e-11;2.5e-06, DCS and GoDARTS, respectively; Homeostatic model assessment 2 (HOMA2)-B; -IR; -S, p=0.0008;4.2e-11;1.1e-09, only in DCS). The main molecular signatures separating the two groups included triacylglycerols, sphingomyelin, testican-1 and interleukin 18 receptor. Conclusions Using an unsupervised network-based fusion method on plasma lipidomics and proteomics data from two independent cohorts, we were able to identify two subgroups of T2D patients differing in terms of disease severity. The molecular signatures identified within these subgroups provide insights into disease mechanisms and possibly new prognostic markers for T2D.
Collapse
Affiliation(s)
- Shiying Li
- Centre de Recherche du CHUM, Faculty of Medicine, University of Montreal, Montreal, QC, Canada
| | - Iulian Dragan
- Vital-IT Group, SIB Swiss Institute of Bioinformatics, Lausanne, Switzerland
| | - Van Du T. Tran
- Vital-IT Group, SIB Swiss Institute of Bioinformatics, Lausanne, Switzerland
| | - Chun Ho Fung
- Section of Cell Biology and Functional Genomics, Department of Metabolism, Diabetes and Reproduction, Imperial College of London, London, United Kingdom
| | - Dmitry Kuznetsov
- Vital-IT Group, SIB Swiss Institute of Bioinformatics, Lausanne, Switzerland
| | | | - Joline W. J. Beulens
- Department of Epidemiology and Data Sciences, Amsterdam University Medical Center, Amsterdam, Netherlands
- Amsterdam Public Health, Amsterdam, Netherlands
| | - Leen M. ‘t Hart
- Department of Epidemiology and Data Sciences, Amsterdam University Medical Center, Amsterdam, Netherlands
- Amsterdam Public Health, Amsterdam, Netherlands
- Department of Cell and Chemical Biology, Leiden University Medical Center, Leiden, Netherlands
- Department of Biomedical Data Sciences, Section Molecular Epidemiology, Leiden University Medical Center, Leiden, Netherlands
| | - Roderick C. Slieker
- Department of Cell and Chemical Biology, Leiden University Medical Center, Leiden, Netherlands
| | - Louise A. Donnelly
- Division of Population Health & Genomics, School of Medicine, University of Dundee, Dundee, United Kingdom
| | | | | | - Florence Mehl
- Vital-IT Group, SIB Swiss Institute of Bioinformatics, Lausanne, Switzerland
| | | | - Petra J. M. Elders
- Department of General Practice and Elderly Care Medicine, Amsterdam Public Health Research Institute, Amsterdam UMC–location VUmc, Amsterdam, Netherlands
| | - Ewan R. Pearson
- Division of Population Health & Genomics, School of Medicine, University of Dundee, Dundee, United Kingdom
| | - Guy A. Rutter
- Centre de Recherche du CHUM, Faculty of Medicine, University of Montreal, Montreal, QC, Canada
- Section of Cell Biology and Functional Genomics, Department of Metabolism, Diabetes and Reproduction, Imperial College of London, London, United Kingdom
- Lee Kong Chian School of Medicine, Nan Yang Technological University, Singapore, Singapore
| | - Mark Ibberson
- Vital-IT Group, SIB Swiss Institute of Bioinformatics, Lausanne, Switzerland
| |
Collapse
|
3
|
Drucker DJ. Prevention of cardiorenal complications in people with type 2 diabetes and obesity. Cell Metab 2024; 36:338-353. [PMID: 38198966 DOI: 10.1016/j.cmet.2023.12.018] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/16/2023] [Revised: 12/06/2023] [Accepted: 12/13/2023] [Indexed: 01/12/2024]
Abstract
Traditional approaches to prevention of the complications of type 2 diabetes (T2D) and obesity have focused on reduction of blood glucose and body weight. The development of new classes of medications, together with evidence from dietary weight loss and bariatric surgery trials, provides new options for prevention of heart failure, chronic kidney disease, myocardial infarction, stroke, metabolic liver disease, cancer, T2D, and neurodegenerative disorders. Here I review evidence for use of lifestyle modification, SGLT-2 inhibitors, GLP-1 receptor agonists, selective mineralocorticoid receptor antagonists, and bariatric surgery, for prevention of cardiorenal and metabolic complications in people with T2D or obesity, highlighting the contributions of weight loss, as well as weight loss-independent mechanisms of action. Collectively, the evidence supports a tailored approach to selection of therapeutic interventions for T2D and obesity based on the likelihood of developing specific complications, rather than a stepwise approach focused exclusively on glycemic or weight control.
Collapse
Affiliation(s)
- Daniel Joshua Drucker
- The Department of Medicine, Lunenfeld-Tanenbaum Research Institute, Mt. Sinai Hospital, University of Toronto, Toronto, ON M5G1X5, Canada.
| |
Collapse
|
4
|
Dwibedi C, Ekström O, Brandt J, Adiels M, Franzén S, Abrahamsson B, Rosengren AH. Randomized open-label trial of semaglutide and dapagliflozin in patients with type 2 diabetes of different pathophysiology. Nat Metab 2024; 6:50-60. [PMID: 38177805 PMCID: PMC10822775 DOI: 10.1038/s42255-023-00943-3] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/09/2023] [Accepted: 11/08/2023] [Indexed: 01/06/2024]
Abstract
The limited understanding of the heterogeneity in the treatment response to antidiabetic drugs contributes to metabolic deterioration and cardiovascular complications1,2, stressing the need for more personalized treatment1. Although recent attempts have been made to classify diabetes into subgroups, the utility of such stratification in predicting treatment response is unknown3. We enrolled participants with type 2 diabetes (n = 239, 74 women and 165 men) and features of severe insulin-deficient diabetes (SIDD) or severe insulin-resistant diabetes (SIRD). Participants were randomly assigned to treatment with the glucagon-like peptide 1 receptor agonist semaglutide or the sodium-glucose cotransporter 2 inhibitor dapagliflozin for 6 months (open label). The primary endpoint was the change in glycated haemoglobin (HbA1c). Semaglutide induced a larger reduction in HbA1c levels than dapagliflozin (mean difference, 8.2 mmol mol-1; 95% confidence interval, -10.0 to -6.3 mmol mol-1), with a pronounced effect in those with SIDD. No difference in adverse events was observed between participants with SIDD and those with SIRD. Analysis of secondary endpoints showed greater reductions in fasting and postprandial glucose concentrations in response to semaglutide in participants with SIDD than in those with SIRD and a more pronounced effect on postprandial glucose by dapagliflozin in participants with SIDD than in those with SIRD. However, no significant interaction was found between drug assignment and the SIDD or SIRD subgroup. In contrast, continuous measures of body mass index, blood pressure, insulin secretion and insulin resistance were useful in identifying those likely to have the largest improvements in glycaemic control and cardiovascular risk factors by adding semaglutide or dapagliflozin. Thus, systematic evaluation of continuous pathophysiological variables can guide the prediction of the treatment response to these drugs and provide more information than stratified subgroups ( NCT04451837 ).
Collapse
Affiliation(s)
- Chinmay Dwibedi
- Department of Neuroscience and Physiology, Sahlgrenska Academy at the University of Gothenburg, Gothenburg, Sweden
- Institute of Medicine, Sahlgrenska Academy at the University of Gothenburg, Gothenburg, Sweden
| | - Ola Ekström
- Department of Clinical Sciences, Diabetes and Endocrinology, Lund University, Malmö, Sweden
| | - Jasmine Brandt
- Department of Clinical Chemistry and Pharmacology, Skåne University Hospital, Lund, Sweden
- Clinical Studies Sweden, Forum South, Skåne University Hospital, Lund, Sweden
| | - Martin Adiels
- Institute of Medicine, Sahlgrenska Academy at the University of Gothenburg, Gothenburg, Sweden
| | - Stefan Franzén
- Institute of Medicine, Sahlgrenska Academy at the University of Gothenburg, Gothenburg, Sweden
- AstraZeneca, Gothenburg, Sweden
| | - Birgitta Abrahamsson
- Department of Neuroscience and Physiology, Sahlgrenska Academy at the University of Gothenburg, Gothenburg, Sweden
| | - Anders H Rosengren
- Department of Neuroscience and Physiology, Sahlgrenska Academy at the University of Gothenburg, Gothenburg, Sweden.
| |
Collapse
|
5
|
Schroeder P, Mandla R, Huerta-Chagoya A, Alkanak A, Nagy D, Szczerbinski L, Madsen JGS, Cole JB, Porneala B, Westerman K, Li JH, Pollin TI, Florez JC, Gloyn AL, Cebola I, Manning A, Leong A, Udler M, Mercader JM. Rare variant association analysis in 51,256 type 2 diabetes cases and 370,487 controls informs the spectrum of pathogenicity of monogenic diabetes genes. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.09.28.23296244. [PMID: 37808701 PMCID: PMC10557807 DOI: 10.1101/2023.09.28.23296244] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/10/2023]
Abstract
We meta-analyzed array data imputed with the TOPMed reference panel and whole-genome sequence (WGS) datasets and performed the largest, rare variant (minor allele frequency as low as 5×10-5) GWAS meta-analysis of type 2 diabetes (T2D) comprising 51,256 cases and 370,487 controls. We identified 52 novel variants at genome-wide significance (p<5 × 10-8), including 8 novel variants that were either rare or ancestry-specific. Among them, we identified a rare missense variant in HNF4A p.Arg114Trp (OR=8.2, 95% confidence interval [CI]=4.6-14.0, p = 1.08×10-13), previously reported as a variant implicated in Maturity Onset Diabetes of the Young (MODY) with incomplete penetrance. We demonstrated that the diabetes risk in carriers of this variant was modulated by a T2D common variant polygenic risk score (cvPRS) (carriers in the top PRS tertile [OR=18.3, 95%CI=7.2-46.9, p=1.2×10-9] vs carriers in the bottom PRS tertile [OR=2.6, 95% CI=0.97-7.09, p = 0.06]. Association results identified eight variants of intermediate penetrance (OR>5) in monogenic diabetes (MD), which in aggregate as a rare variant PRS were associated with T2D in an independent WGS dataset (OR=4.7, 95% CI=1.86-11.77], p = 0.001). Our data also provided support evidence for 21% of the variants reported in ClinVar in these MD genes as benign based on lack of association with T2D. Our work provides a framework for using rare variant imputation and WGS analyses in large-scale population-based association studies to identify large-effect rare variants and provide evidence for informing variant pathogenicity.
Collapse
Affiliation(s)
- Philip Schroeder
- Programs in Metabolism and Medical & Population Genetics, Broad Institute of Harvard and MIT, Cambridge, MA, USA
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
- Diabetes Unit, Massachusetts General Hospital, Boston, MA, USA
| | - Ravi Mandla
- Programs in Metabolism and Medical & Population Genetics, Broad Institute of Harvard and MIT, Cambridge, MA, USA
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
- Diabetes Unit, Massachusetts General Hospital, Boston, MA, USA
- Department of Medicine and Cardiovascular Research Institute, Cardiology Division, University of California, San Francisco, CA, USA
| | - Alicia Huerta-Chagoya
- Programs in Metabolism and Medical & Population Genetics, Broad Institute of Harvard and MIT, Cambridge, MA, USA
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
| | - Ahmed Alkanak
- Programs in Metabolism and Medical & Population Genetics, Broad Institute of Harvard and MIT, Cambridge, MA, USA
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
| | - Dorka Nagy
- Section of Genetics and Genomics, Department of Metabolism, Digestion and Reproduction, Imperial College London, London, UK
- National Heart and Lung Institute, Faculty of Medicine, London, UK
| | - Lukasz Szczerbinski
- Department of Endocrinology, Diabetology and Internal Medicine, Medical University of Bialystok, Bialystok, 15-276, Poland
- Clinical Research Centre, Medical University of Bialystok, Bialystok, 15-276, Poland
- Programs in Metabolism and Medical & Population Genetics, Broad Institute of Harvard and MIT, Cambridge, MA, USA
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
- Diabetes Unit, Massachusetts General Hospital, Boston, MA, USA
| | - Jesper G S Madsen
- Institute of Mathematics and Computer Science, University of Southern Denmark, Odense M, 5230, Denmark
- The Novo Nordisk Foundation Center for Genomic Mechanisms of Disease, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Joanne B Cole
- Programs in Metabolism and Medical & Population Genetics, Broad Institute of Harvard and MIT, Cambridge, MA, USA
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
- Department of Medicine, Harvard Medical School, Boston, MA, USA
- Division of Endocrinology, Boston Children's Hospital, Boston, MA, USA
- Department of Biomedical Informatics, University of Colorado School of Medicine, Aurora, CO, 80045, USA
| | - Bianca Porneala
- Division of General Internal Medicine, Massachusetts General Hospital, Boston, MA, USA
| | - Kenneth Westerman
- Programs in Metabolism and Medical & Population Genetics, Broad Institute of Harvard and MIT, Cambridge, MA, USA
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
- Department of Medicine, Massachusetts General Hospital, Boston, MA, USA
| | - Josephine H Li
- Programs in Metabolism and Medical & Population Genetics, Broad Institute of Harvard and MIT, Cambridge, MA, USA
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
- Diabetes Unit, Massachusetts General Hospital, Boston, MA, USA
- Department of Medicine, Massachusetts General Hospital, Boston, MA, USA
| | - Toni I Pollin
- Emory University, Atlanta, Georgia, USA., Atlanta, GA, USA
| | - Jose C Florez
- Programs in Metabolism and Medical & Population Genetics, Broad Institute of Harvard and MIT, Cambridge, MA, USA
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
- Diabetes Unit, Massachusetts General Hospital, Boston, MA, USA
- Department of Medicine, Massachusetts General Hospital, Boston, MA, USA
- Endocrine Division, Massachusetts General Hospital, Boston, MA, USA
- Department of Medicine, Harvard Medical School, Boston, MA, USA
| | - Anna L Gloyn
- Department of Pediatrics, Division of Endocrinology, Stanford School of Medicine, Stanford, CA, USA
| | - Inês Cebola
- Section of Genetics and Genomics, Department of Metabolism, Digestion and Reproduction, Imperial College London, London, UK
| | - Alisa Manning
- Programs in Metabolism and Medical & Population Genetics, Broad Institute of Harvard and MIT, Cambridge, MA, USA
- Clinical and Translational Epidemiology Unit, Massachusetts General Hospital, Boston, MA, USA
- Department of Medicine, Harvard Medical School, Boston, MA, USA
| | - Aaron Leong
- Programs in Metabolism and Medical & Population Genetics, Broad Institute of Harvard and MIT, Cambridge, MA, USA
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
- Diabetes Unit, Massachusetts General Hospital, Boston, MA, USA
- Department of Medicine, Massachusetts General Hospital, Boston, MA, USA
- Endocrine Division, Massachusetts General Hospital, Boston, MA, USA
- Division of General Internal Medicine, Massachusetts General Hospital, Boston, MA, USA
| | - Miriam Udler
- Programs in Metabolism and Medical & Population Genetics, Broad Institute of Harvard and MIT, Cambridge, MA, USA
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
- Diabetes Unit, Massachusetts General Hospital, Boston, MA, USA
- Department of Medicine, Harvard Medical School, Boston, MA, USA
- Department of Medicine, Massachusetts General Hospital, Boston, MA, USA
| | - Josep M Mercader
- Programs in Metabolism and Medical & Population Genetics, Broad Institute of Harvard and MIT, Cambridge, MA, USA
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
- Diabetes Unit, Massachusetts General Hospital, Boston, MA, USA
- Department of Medicine, Harvard Medical School, Boston, MA, USA
| |
Collapse
|
6
|
Kuss O, Opitz ME, Brandstetter LV, Schlesinger S, Roden M, Hoyer A. How amenable is type 2 diabetes treatment for precision diabetology? A meta-regression of glycaemic control data from 174 randomised trials. Diabetologia 2023; 66:1622-1632. [PMID: 37338539 PMCID: PMC10390610 DOI: 10.1007/s00125-023-05951-2] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/06/2023] [Accepted: 05/16/2023] [Indexed: 06/21/2023]
Abstract
AIMS/HYPOTHESIS There are two prerequisites for the precision medicine approach to be beneficial for treated individuals. First, there must be treatment heterogeneity; second, in the case of treatment heterogeneity, we need to detect clinical predictors to identify people who would benefit from one treatment more than from others. There is an established meta-regression approach to assess these two prerequisites that relies on measuring the variability of a clinical outcome after treatment in placebo-controlled randomised trials. Our aim was to apply this approach to the treatment of type 2 diabetes. METHODS We performed a meta-regression analysis using information from 174 placebo-controlled randomised trials with 178 placebo and 272 verum (i.e. active treatment) arms including 86,940 participants with respect to the variability of glycaemic control as assessed by HbA1c after treatment and its potential predictors. RESULTS The adjusted difference in log(SD) values between the verum and placebo arms was 0.037 (95% CI: 0.004, 0.069). That is, we found a small increase in the variability of HbA1c values after treatment in the verum arms. In addition, one potentially relevant predictor for explaining this increase, drug class, was observed, and GLP-1 receptor agonists yielded the largest differences in log(SD) values. CONCLUSIONS/INTERPRETATION The potential of the precision medicine approach in the treatment of type 2 diabetes is modest at best, at least with regard to an improvement in glycaemic control. Our finding of a larger variability after treatment with GLP-1 receptor agonists in individuals with poor glycaemic control should be replicated and/or validated with other clinical outcomes and with different study designs. FUNDING The research reported here received no specific grant from any funding agency in the public, commercial or not-for-profit sectors. DATA AVAILABILITY Two datasets (one for the log[SD] and one for the baseline-corrected log[SD]) to reproduce the analyses from this paper are available on https://zenodo.org/record/7956635 .
Collapse
Affiliation(s)
- Oliver Kuss
- Institute for Biometrics and Epidemiology, German Diabetes Center, Leibniz Institute for Diabetes Research at Heinrich Heine University Düsseldorf, Düsseldorf, Germany.
- Centre for Health and Society, Faculty of Medicine, Heinrich Heine University Düsseldorf, Düsseldorf, Germany.
- German Center for Diabetes Research, Partner Düsseldorf, München-Neuherberg, Germany.
| | | | | | - Sabrina Schlesinger
- Institute for Biometrics and Epidemiology, German Diabetes Center, Leibniz Institute for Diabetes Research at Heinrich Heine University Düsseldorf, Düsseldorf, Germany
- German Center for Diabetes Research, Partner Düsseldorf, München-Neuherberg, Germany
| | - Michael Roden
- German Center for Diabetes Research, Partner Düsseldorf, München-Neuherberg, Germany
- Institute for Clinical Diabetology, German Diabetes Center, Leibniz Center for Diabetes Research at Heinrich Heine University Düsseldorf, Düsseldorf, Germany
- Department of Endocrinology and Diabetology, Medical Faculty and University Hospital Düsseldorf, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
| | - Annika Hoyer
- Biostatistics and Medical Biometry, Medical School EWL, Bielefeld University, Bielefeld, Germany
| |
Collapse
|
7
|
Forteath C, Mordi I, Nisr R, Gutierrez-Lara EJ, Alqurashi N, Phair IR, Cameron AR, Beall C, Bahr I, Mohan M, Wong AKF, Dihoum A, Mohammad A, Palmer CNA, Lamont D, Sakamoto K, Viollet B, Foretz M, Lang CC, Rena G. Amino acid homeostasis is a target of metformin therapy. Mol Metab 2023; 74:101750. [PMID: 37302544 PMCID: PMC10328998 DOI: 10.1016/j.molmet.2023.101750] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/25/2023] [Revised: 04/04/2023] [Accepted: 06/05/2023] [Indexed: 06/13/2023] Open
Abstract
OBJECTIVE Unexplained changes in regulation of branched chain amino acids (BCAA) during diabetes therapy with metformin have been known for years. Here we have investigated mechanisms underlying this effect. METHODS We used cellular approaches, including single gene/protein measurements, as well as systems-level proteomics. Findings were then cross-validated with electronic health records and other data from human material. RESULTS In cell studies, we observed diminished uptake/incorporation of amino acids following metformin treatment of liver cells and cardiac myocytes. Supplementation of media with amino acids attenuated known effects of the drug, including on glucose production, providing a possible explanation for discrepancies between effective doses in vivo and in vitro observed in most studies. Data-Independent Acquisition proteomics identified that SNAT2, which mediates tertiary control of BCAA uptake, was the most strongly suppressed amino acid transporter in liver cells following metformin treatment. Other transporters were affected to a lesser extent. In humans, metformin attenuated increased risk of left ventricular hypertrophy due to the AA allele of KLF15, which is an inducer of BCAA catabolism. In plasma from a double-blind placebo-controlled trial in nondiabetic heart failure (trial registration: NCT00473876), metformin caused selective accumulation of plasma BCAA and glutamine, consistent with the effects in cells. CONCLUSIONS Metformin restricts tertiary control of BCAA cellular uptake. We conclude that modulation of amino acid homeostasis contributes to therapeutic actions of the drug.
Collapse
Affiliation(s)
- Calum Forteath
- Division of Cellular and Systems Medicine, Ninewells Hospital and Medical School, University of Dundee, Dundee, Scotland, DD1 9SY, UK
| | - Ify Mordi
- Division of Molecular and Clinical Medicine, Ninewells Hospital and Medical School, University of Dundee, Dundee, Scotland, DD1 9SY, UK
| | - Raid Nisr
- Division of Cellular and Systems Medicine, Ninewells Hospital and Medical School, University of Dundee, Dundee, Scotland, DD1 9SY, UK
| | - Erika J Gutierrez-Lara
- Division of Cellular and Systems Medicine, Ninewells Hospital and Medical School, University of Dundee, Dundee, Scotland, DD1 9SY, UK
| | - Noor Alqurashi
- Division of Cellular and Systems Medicine, Ninewells Hospital and Medical School, University of Dundee, Dundee, Scotland, DD1 9SY, UK
| | - Iain R Phair
- Division of Cellular and Systems Medicine, Ninewells Hospital and Medical School, University of Dundee, Dundee, Scotland, DD1 9SY, UK
| | - Amy R Cameron
- Division of Cellular and Systems Medicine, Ninewells Hospital and Medical School, University of Dundee, Dundee, Scotland, DD1 9SY, UK; Department of Clinical and Biomedical Sciences, Faculty of Health and Life Sciences, University of Exeter, RILD Building, Exeter, EX2 5DW, UK
| | - Craig Beall
- Department of Clinical and Biomedical Sciences, Faculty of Health and Life Sciences, University of Exeter, RILD Building, Exeter, EX2 5DW, UK
| | - Ibrahim Bahr
- Division of Cellular and Systems Medicine, Ninewells Hospital and Medical School, University of Dundee, Dundee, Scotland, DD1 9SY, UK
| | - Mohapradeep Mohan
- Division of Molecular and Clinical Medicine, Ninewells Hospital and Medical School, University of Dundee, Dundee, Scotland, DD1 9SY, UK
| | - Aaron K F Wong
- Division of Molecular and Clinical Medicine, Ninewells Hospital and Medical School, University of Dundee, Dundee, Scotland, DD1 9SY, UK
| | - Adel Dihoum
- Division of Cellular and Systems Medicine, Ninewells Hospital and Medical School, University of Dundee, Dundee, Scotland, DD1 9SY, UK; Division of Molecular and Clinical Medicine, Ninewells Hospital and Medical School, University of Dundee, Dundee, Scotland, DD1 9SY, UK
| | - Anwar Mohammad
- Public Health and Epidemiology Department, Dasman Diabetes Institute, Kuwait City, Kuwait
| | - Colin N A Palmer
- Division of Population Health and Genomics, Ninewells Hospital and Medical School, University of Dundee, Dundee, Scotland, DD1 9SY, UK
| | - Douglas Lamont
- Centre for Advanced Scientific Technologies, School of Life Sciences, University of Dundee, Dundee, DD1 5EH, UK
| | - Kei Sakamoto
- Novo Nordisk Foundation Center for Basic Metabolic Research, University of Copenhagen, Copenhagen, 2200, Denmark
| | - Benoit Viollet
- Université Paris Cité, CNRS, Inserm, Institut Cochin, Paris, 75014, France
| | - Marc Foretz
- Université Paris Cité, CNRS, Inserm, Institut Cochin, Paris, 75014, France
| | - Chim C Lang
- Division of Molecular and Clinical Medicine, Ninewells Hospital and Medical School, University of Dundee, Dundee, Scotland, DD1 9SY, UK.
| | - Graham Rena
- Division of Cellular and Systems Medicine, Ninewells Hospital and Medical School, University of Dundee, Dundee, Scotland, DD1 9SY, UK.
| |
Collapse
|
8
|
Vargas E, Nandhakumar P, Ding S, Saha T, Wang J. Insulin detection in diabetes mellitus: challenges and new prospects. Nat Rev Endocrinol 2023:10.1038/s41574-023-00842-3. [PMID: 37217746 DOI: 10.1038/s41574-023-00842-3] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 04/19/2023] [Indexed: 05/24/2023]
Abstract
Tremendous progress has been made towards achieving tight glycaemic control in individuals with diabetes mellitus through the use of frequent or continuous glucose measurements. However, in patients who require insulin, accurate dosing must consider multiple factors that affect insulin sensitivity and modulate insulin bolus needs. Accordingly, an urgent need exists for frequent and real-time insulin measurements to closely track the dynamic blood concentration of insulin during insulin therapy and guide optimal insulin dosing. Nevertheless, traditional centralized insulin testing cannot offer timely measurements, which are essential to achieving this goal. This Perspective discusses the advances and challenges in moving insulin assays from traditional laboratory-based assays to frequent and continuous measurements in decentralized (point-of-care and home) settings. Technologies that hold promise for insulin testing using disposable test strips, mobile systems and wearable real-time insulin-sensing devices are discussed. We also consider future prospects for continuous insulin monitoring and for fully integrated multisensor-guided closed-loop artificial pancreas systems.
Collapse
Affiliation(s)
- Eva Vargas
- Department of Nanoengineering, University of California San Diego, La Jolla, CA, USA
| | - Ponnusamy Nandhakumar
- Department of Nanoengineering, University of California San Diego, La Jolla, CA, USA
| | - Shichao Ding
- Department of Nanoengineering, University of California San Diego, La Jolla, CA, USA
| | - Tamoghna Saha
- Department of Nanoengineering, University of California San Diego, La Jolla, CA, USA
| | - Joseph Wang
- Department of Nanoengineering, University of California San Diego, La Jolla, CA, USA.
| |
Collapse
|
9
|
Michalek DA, Onengut-Gumuscu S, Repaske DR, Rich SS. Precision Medicine in Type 1 Diabetes. J Indian Inst Sci 2023; 103:335-351. [PMID: 37538198 PMCID: PMC10393845 DOI: 10.1007/s41745-023-00356-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/18/2022] [Accepted: 01/04/2023] [Indexed: 03/09/2023]
Abstract
Type 1 diabetes is a complex, chronic disease in which the insulin-producing beta cells in the pancreas are sufficiently altered or impaired to result in requirement of exogenous insulin for survival. The development of type 1 diabetes is thought to be an autoimmune process, in which an environmental (unknown) trigger initiates a T cell-mediated immune response in genetically susceptible individuals. The presence of islet autoantibodies in the blood are signs of type 1 diabetes development, and risk of progressing to clinical type 1 diabetes is correlated with the presence of multiple islet autoantibodies. Currently, a "staging" model of type 1 diabetes proposes discrete components consisting of normal blood glucose but at least two islet autoantibodies (Stage 1), abnormal blood glucose with at least two islet autoantibodies (Stage 2), and clinical diagnosis (Stage 3). While these stages may, in fact, not be discrete and vary by individual, the format suggests important applications of precision medicine to diagnosis, prevention, prognosis, treatment and monitoring. In this paper, applications of precision medicine in type 1 diabetes are discussed, with both opportunities and barriers to global implementation highlighted. Several groups have implemented components of precision medicine, yet the integration of the necessary steps to achieve both short- and long-term solutions will need to involve researchers, patients, families, and healthcare providers to fully impact and reduce the burden of type 1 diabetes.
Collapse
Affiliation(s)
- Dominika A. Michalek
- Center for Public Health Genomics, University of Virginia, Charlottesville, VA USA
| | - Suna Onengut-Gumuscu
- Center for Public Health Genomics, University of Virginia, Charlottesville, VA USA
- Department of Public Health Sciences, University of Virginia, Charlottesville, VA USA
| | - David R. Repaske
- Division of Endocrinology, Department of Pediatrics, University of Virginia, Charlottesville, VA USA
| | - Stephen S. Rich
- Center for Public Health Genomics, University of Virginia, Charlottesville, VA USA
- Department of Public Health Sciences, University of Virginia, Charlottesville, VA USA
| |
Collapse
|
10
|
Mannino GC, Mancuso E, Sbrignadello S, Morettini M, Andreozzi F, Tura A. Chemical Compounds and Ambient Factors Affecting Pancreatic Alpha-Cells Mass and Function: What Evidence? INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2022; 19:16489. [PMID: 36554367 PMCID: PMC9778390 DOI: 10.3390/ijerph192416489] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/28/2022] [Revised: 12/02/2022] [Accepted: 12/04/2022] [Indexed: 06/17/2023]
Abstract
The exposure to different substances present in the environment can affect the ability of the human body to maintain glucose homeostasis. Some review studies summarized the current evidence about the relationships between environment and insulin resistance or beta-cell dysfunction. Instead, no reviews focused on the relationships between the environment and the alpha cell, although in recent years clear indications have emerged for the pivotal role of the alpha cell in glucose regulation. Thus, the aim of this review was to analyze the studies about the effects of chemical, biological, and physical environmental factors on the alpha cell. Notably, we found studies focusing on the effects of different categories of compounds, including air pollutants, compounds of known toxicity present in common objects, pharmacological agents, and compounds possibly present in food, plus studies on the effects of physical factors (mainly heat exposure). However, the overall number of relevant studies was limited, especially when compared to studies related to the environment and insulin sensitivity or beta-cell function. In our opinion, this was likely due to the underestimation of the alpha-cell role in glucose homeostasis, but since such a role has recently emerged with increasing strength, we expect several new studies about the environment and alpha-cell in the near future.
Collapse
Affiliation(s)
- Gaia Chiara Mannino
- Department of Medical and Surgical Sciences, Magna Graecia University of Catanzaro, 88100 Catanzaro, Italy
| | - Elettra Mancuso
- Department of Medical and Surgical Sciences, Magna Graecia University of Catanzaro, 88100 Catanzaro, Italy
| | | | - Micaela Morettini
- Department of Information Engineering, Università Politecnica delle Marche, 60131 Ancona, Italy
| | - Francesco Andreozzi
- Department of Medical and Surgical Sciences, Magna Graecia University of Catanzaro, 88100 Catanzaro, Italy
| | - Andrea Tura
- CNR Institute of Neuroscience, 35127 Padova, Italy
| |
Collapse
|
11
|
Krook A, Mulder H. Pinpointing precision medicine for diabetes mellitus. Diabetologia 2022; 65:1755-1757. [PMID: 35997779 DOI: 10.1007/s00125-022-05777-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/01/2022]
Affiliation(s)
- Anna Krook
- Department of Physiology and Pharmacology, Section of Integrative Physiology, Karolinska Institutet, Stockholm, Sweden.
| | - Hindrik Mulder
- Unit of Molecular Metabolism, Lund University Diabetes Centre, Malmö, Sweden
| |
Collapse
|
12
|
Abstract
First envisioned by early diabetes clinicians, a person-centred approach to care was an aspirational goal that aimed to match insulin therapy to each individual's unique requirements. In the 100 years since the discovery of insulin, this goal has evolved to include personalised approaches to type 1 diabetes diagnosis, treatment, prevention and prediction. These advances have been facilitated by the recognition of type 1 diabetes as an autoimmune disease and by advances in our understanding of diabetes pathophysiology, genetics and natural history, which have occurred in parallel with advancements in insulin delivery, glucose monitoring and tools for self-management. In this review, we discuss how these personalised approaches have improved diabetes care and how improved understanding of pathogenesis and human biology might inform precision medicine in the future.
Collapse
Affiliation(s)
- Alice L J Carr
- Institute of Biomedical and Clinical Science, University of Exeter Medical School, Exeter, UK.
| | - Carmella Evans-Molina
- Department of Pediatrics, Indiana University School of Medicine, Indianapolis, IN, USA
- Department of Medicine, Indiana University School of Medicine, Indianapolis, IN, USA
- Department of Anatomy, Cell Biology, and Physiology, Indiana University School of Medicine, Indianapolis, IN, USA
- Department of Biochemistry and Molecular Biology, Indiana University School of Medicine, Indianapolis, IN, USA
- Center for Diabetes and Metabolic Diseases, Indiana University School of Medicine, Indianapolis, IN, USA
- Herman B. Wells Center for Pediatric Research, Indiana University School of Medicine, Indianapolis, IN, USA
- Richard L. Roudebush VA Medical Center, Indianapolis, IN, USA
| | - Richard A Oram
- Institute of Biomedical and Clinical Science, University of Exeter Medical School, Exeter, UK.
| |
Collapse
|
13
|
Pipal KV, Mamtani M, Patel AA, Jaiswal SG, Jaisinghani MT, Kulkarni H. Susceptibility Loci for Type 2 Diabetes in the Ethnically Endogamous Indian Sindhi Population: A Pooled Blood Genome-Wide Association Study. Genes (Basel) 2022; 13:genes13081298. [PMID: 35893037 PMCID: PMC9331904 DOI: 10.3390/genes13081298] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2022] [Revised: 07/15/2022] [Accepted: 07/19/2022] [Indexed: 12/10/2022] Open
Abstract
Type 2 diabetes (T2D) is a complex metabolic derangement that has a strong genetic basis. There is substantial population-specificity in the association of genetic variants with T2D. The Indian urban Sindhi population is at a high risk of T2D. The genetic basis of T2D in this population is unknown. We interrogated 28 pooled whole blood genomes of 1402 participants from the Diabetes In Sindhi Families In Nagpur (DISFIN) study using Illumina’s Global Screening Array. From a total of 608,550 biallelic variants, 140 were significantly associated with T2D after adjusting for comorbidities, batch effects, pooling error, kinship status and pooling variation in a random effects multivariable logistic regression framework. Of the 102 well-characterized genes that these variants mapped onto, 70 genes have been previously reported to be associated with T2D to varying degrees with known functional relevance. Excluding open reading frames, intergenic non-coding elements and pseudogenes, our study identified 22 novel candidate genes in the Sindhi population studied. Our study thus points to the potential, interesting candidate genes associated with T2D in an ethnically endogamous population. These candidate genes need to be fully investigated in future studies.
Collapse
Affiliation(s)
- Kanchan V. Pipal
- Lata Medical Research Foundation, Nagpur 440002, India; (K.V.P.); (M.M.); (A.A.P.); (S.G.J.); (M.T.J.)
| | - Manju Mamtani
- Lata Medical Research Foundation, Nagpur 440002, India; (K.V.P.); (M.M.); (A.A.P.); (S.G.J.); (M.T.J.)
- M&H Research, LLC, San Antonio, TX 78249, USA
| | - Ashwini A. Patel
- Lata Medical Research Foundation, Nagpur 440002, India; (K.V.P.); (M.M.); (A.A.P.); (S.G.J.); (M.T.J.)
| | - Sujeet G. Jaiswal
- Lata Medical Research Foundation, Nagpur 440002, India; (K.V.P.); (M.M.); (A.A.P.); (S.G.J.); (M.T.J.)
| | - Manisha T. Jaisinghani
- Lata Medical Research Foundation, Nagpur 440002, India; (K.V.P.); (M.M.); (A.A.P.); (S.G.J.); (M.T.J.)
| | - Hemant Kulkarni
- Lata Medical Research Foundation, Nagpur 440002, India; (K.V.P.); (M.M.); (A.A.P.); (S.G.J.); (M.T.J.)
- M&H Research, LLC, San Antonio, TX 78249, USA
- Correspondence:
| |
Collapse
|